{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heart Disease UCI**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import HTML\n",
    "# HTML('''\n",
    "# <script>\n",
    "#   function code_toggle() {\n",
    "#     if (code_shown){\n",
    "#       $('div.input').hide('500');\n",
    "#       $('#toggleButton').val('Show Code')\n",
    "#     } else {\n",
    "#       $('div.input').show('500');\n",
    "#       $('#toggleButton').val('Hide Code')\n",
    "#     }\n",
    "#     code_shown = !code_shown\n",
    "#   }\n",
    "\n",
    "#   $( document ).ready(function(){\n",
    "#     code_shown=true;\n",
    "#   });\n",
    "# </script>\n",
    "# <form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Hide Code\"></form>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['heart.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  sex  cp  trestbps  chol   ...    oldpeak  slope  ca  thal  target\n",
      "0   63    1   3       145   233   ...        2.3      0   0     1       1\n",
      "1   37    1   2       130   250   ...        3.5      0   0     2       1\n",
      "2   41    0   1       130   204   ...        1.4      2   0     2       1\n",
      "3   56    1   1       120   236   ...        0.8      2   0     2       1\n",
      "4   57    0   0       120   354   ...        0.6      2   0     2       1\n",
      "\n",
      "[5 rows x 14 columns]\n",
      "(303, 14)\n",
      "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"../input/heart.csv\")\n",
    "#df = df.drop('Unnamed: 0', axis=1)\n",
    "print(df.head())\n",
    "print(df.shape)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Male (1) vs Female (0) affected by Heart Diseases')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.countplot(x = 'target',data = df,hue = 'sex')\n",
    "plt.title(\"Male (1) vs Female (0) affected by Heart Diseases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe252a9c6a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,12))\n",
    "sns.countplot(x = 'age',hue = 'target',data = df)\n",
    "plt.title(\"People affected by heart deseases vs age\", fontsize=20)\n",
    "plt.legend([\"Healthy\",\"Ill\"], fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Heart rate vs Age')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "plt.scatter(x = 'age',y = 'thalach', c='target',data = df)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max heart rate')\n",
    "plt.title('Heart rate vs Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2527e7c88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "corr=df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Machine Learning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop(['target'], axis = 1).values\n",
    "Y = df['target']\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing :\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from itertools import product\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[35  9]\n",
      " [ 4 43]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.80      0.84        44\n",
      "           1       0.83      0.91      0.87        47\n",
      "\n",
      "   micro avg       0.86      0.86      0.86        91\n",
      "   macro avg       0.86      0.86      0.86        91\n",
      "weighted avg       0.86      0.86      0.86        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedmodel = LogisticRegression().fit(X_Train,Y_Train)\n",
    "predictions =trainedmodel.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictions))\n",
    "print(classification_report(Y_Test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33 11]\n",
      " [ 6 41]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.75      0.80        44\n",
      "           1       0.79      0.87      0.83        47\n",
      "\n",
      "   micro avg       0.81      0.81      0.81        91\n",
      "   macro avg       0.82      0.81      0.81        91\n",
      "weighted avg       0.82      0.81      0.81        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[34 10]\n",
      " [ 4 43]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.77      0.83        44\n",
      "           1       0.81      0.91      0.86        47\n",
      "\n",
      "   micro avg       0.85      0.85      0.85        91\n",
      "   macro avg       0.85      0.84      0.84        91\n",
      "weighted avg       0.85      0.85      0.85        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28 16]\n",
      " [ 4 43]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.64      0.74        44\n",
      "           1       0.73      0.91      0.81        47\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        91\n",
      "   macro avg       0.80      0.78      0.77        91\n",
      "weighted avg       0.80      0.78      0.78        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionstree))\n",
    "print(classification_report(Y_Test,predictionstree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<graphviz.files.Source at 0x7fe23b9eef28>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "\n",
    "data = export_graphviz(trainedtree,out_file=None,feature_names=df.drop(['target'], axis = 1).columns,\n",
    "                       class_names=['0', '1'],  \n",
    "                       filled=True, rounded=True,  \n",
    "                       max_depth=2,\n",
    "                       special_characters=True)\n",
    "graph = graphviz.Source(data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Discriminant Anaylsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[34 10]\n",
      " [ 3 44]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.77      0.84        44\n",
      "           1       0.81      0.94      0.87        47\n",
      "\n",
      "   micro avg       0.86      0.86      0.86        91\n",
      "   macro avg       0.87      0.85      0.86        91\n",
      "weighted avg       0.87      0.86      0.86        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedlda = LinearDiscriminantAnalysis().fit(X_Train, Y_Train)\n",
    "predictionlda = trainedlda.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionlda))\n",
    "print(classification_report(Y_Test,predictionlda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[36  8]\n",
      " [ 7 40]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.82      0.83        44\n",
      "           1       0.83      0.85      0.84        47\n",
      "\n",
      "   micro avg       0.84      0.84      0.84        91\n",
      "   macro avg       0.84      0.83      0.83        91\n",
      "weighted avg       0.84      0.84      0.84        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainednb = GaussianNB().fit(X_Train, Y_Train)\n",
    "predictionnb = trainednb.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionnb))\n",
    "print(classification_report(Y_Test,predictionnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 3600x3960 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "model = XGBClassifier()\n",
    "\n",
    "# Train\n",
    "model.fit(X_Train, Y_Train)\n",
    "\n",
    "plot_tree(model)\n",
    "plt.figure(figsize = (50,55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33 11]\n",
      " [ 6 41]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.75      0.80        44\n",
      "           1       0.79      0.87      0.83        47\n",
      "\n",
      "   micro avg       0.81      0.81      0.81        91\n",
      "   macro avg       0.82      0.81      0.81        91\n",
      "weighted avg       0.82      0.81      0.81        91\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "import itertools\n",
    "\n",
    "predictions =model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictions))\n",
    "print(classification_report(Y_Test,predictions))\n",
    "\n",
    "# Thanks to: https://www.kaggle.com/tejainece/data-visualization-and-machine-learning-algorithms\n",
    "def plot_confusion_matrix(cm, classes=[\"0\", \"1\"], title=\"\",\n",
    "                          cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title('Confusion matrix ' +title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "cm_plot = confusion_matrix(Y_Test,predictions)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_plot, title = 'XGBClassifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "[[35  9]\n",
      " [ 3 44]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.80      0.85        44\n",
      "           1       0.83      0.94      0.88        47\n",
      "\n",
      "   micro avg       0.87      0.87      0.87        91\n",
      "   macro avg       0.88      0.87      0.87        91\n",
      "weighted avg       0.87      0.87      0.87        91\n",
      "\n",
      "SVM\n",
      "[[35  9]\n",
      " [ 5 42]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.83        44\n",
      "           1       0.82      0.89      0.86        47\n",
      "\n",
      "   micro avg       0.85      0.85      0.85        91\n",
      "   macro avg       0.85      0.84      0.85        91\n",
      "weighted avg       0.85      0.85      0.85        91\n",
      "\n",
      "Random Forest\n",
      "[[34 10]\n",
      " [10 37]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.77      0.77        44\n",
      "           1       0.79      0.79      0.79        47\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        91\n",
      "   macro avg       0.78      0.78      0.78        91\n",
      "weighted avg       0.78      0.78      0.78        91\n",
      "\n",
      "Logistic Regression\n",
      "[[35  9]\n",
      " [ 5 42]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.83        44\n",
      "           1       0.82      0.89      0.86        47\n",
      "\n",
      "   micro avg       0.85      0.85      0.85        91\n",
      "   macro avg       0.85      0.84      0.85        91\n",
      "weighted avg       0.85      0.85      0.85        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2,svd_solver='full')\n",
    "X_pca = pca.fit_transform(X)\n",
    "# print(pca.explained_variance_)\n",
    "\n",
    "X_reduced, X_test_reduced, Y_Train, Y_Test = train_test_split(X_pca, Y, test_size = 0.30, random_state = 101)\n",
    "\n",
    "# pca = PCA(n_components=2,svd_solver='full')\n",
    "# X_reduced = pca.fit_transform(X_Train)\n",
    "#X_reduced = TSNE(n_components=2).fit_transform(X_Train, Y_Train)\n",
    "\n",
    "trainednb = GaussianNB().fit(X_reduced, Y_Train)\n",
    "trainedsvm = svm.LinearSVC().fit(X_reduced, Y_Train)\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_reduced,Y_Train)\n",
    "trainedmodel = LogisticRegression().fit(X_reduced,Y_Train)\n",
    "\n",
    "# pca = PCA(n_components=2,svd_solver='full')\n",
    "# X_test_reduced = pca.fit_transform(X_Test)\n",
    "#X_test_reduced = TSNE(n_components=2).fit_transform(X_Test, Y_Test)\n",
    "\n",
    "print('Naive Bayes')\n",
    "predictionnb = trainednb.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictionnb))\n",
    "print(classification_report(Y_Test,predictionnb))\n",
    "\n",
    "print('SVM')\n",
    "predictionsvm = trainedsvm.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))\n",
    "\n",
    "print('Random Forest')\n",
    "predictionforest = trainedforest.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "\n",
    "print('Logistic Regression')\n",
    "predictions =trainedmodel.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictions))\n",
    "print(classification_report(Y_Test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_data = X_reduced\n",
    "\n",
    "trainednb = GaussianNB().fit(reduced_data, Y_Train)\n",
    "trainedsvm = svm.LinearSVC().fit(reduced_data, Y_Train)\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(reduced_data,Y_Train)\n",
    "trainedmodel = LogisticRegression().fit(reduced_data,Y_Train)\n",
    "\n",
    "# Thanks to: https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html\n",
    "\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n",
    "\n",
    "for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
    "                        [trainednb, trainedsvm, trainedforest, trainedmodel],\n",
    "                        ['Naive Bayes Classifier', 'SVM',\n",
    "                         'Random Forest', 'Logistic Regression']):\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    \n",
    "\n",
    "    axarr[idx[0], idx[1]].contourf(xx, yy, Z,cmap=plt.cm.coolwarm, alpha=0.4)\n",
    "    axarr[idx[0], idx[1]].scatter(reduced_data[:, 0], reduced_data[:, 1], c=Y_Train,\n",
    "                                  s=20, edgecolor='k')\n",
    "    axarr[idx[0], idx[1]].set_title(tt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original number of features: 13\n",
      "Reduced number of features: 1\n",
      "[1.]\n",
      "Naive Bayes\n",
      "[[35  9]\n",
      " [ 3 44]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.80      0.85        44\n",
      "           1       0.83      0.94      0.88        47\n",
      "\n",
      "   micro avg       0.87      0.87      0.87        91\n",
      "   macro avg       0.88      0.87      0.87        91\n",
      "weighted avg       0.87      0.87      0.87        91\n",
      "\n",
      "SVM\n",
      "[[35  9]\n",
      " [ 3 44]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.80      0.85        44\n",
      "           1       0.83      0.94      0.88        47\n",
      "\n",
      "   micro avg       0.87      0.87      0.87        91\n",
      "   macro avg       0.88      0.87      0.87        91\n",
      "weighted avg       0.87      0.87      0.87        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load libraries\n",
    "from sklearn import datasets\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "# Create an LDA that will reduce the data down to 1 feature\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "\n",
    "# run an LDA and use it to transform the features\n",
    "X_lda = lda.fit(X, Y).transform(X)\n",
    "\n",
    "# Print the number of features\n",
    "print('Original number of features:', X.shape[1])\n",
    "print('Reduced number of features:', X_lda.shape[1])\n",
    "\n",
    "## View the ratio of explained variance\n",
    "print(lda.explained_variance_ratio_)\n",
    "\n",
    "X_reduced, X_test_reduced, Y_Train, Y_Test = train_test_split(X_lda, Y, test_size = 0.30, random_state = 101)\n",
    "\n",
    "trainednb = GaussianNB().fit(X_reduced, Y_Train)\n",
    "trainedsvm = svm.LinearSVC().fit(X_reduced, Y_Train)\n",
    "\n",
    "print('Naive Bayes')\n",
    "predictionnb = trainednb.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictionnb))\n",
    "print(classification_report(Y_Test,predictionnb))\n",
    "\n",
    "print('SVM')\n",
    "predictionsvm = trainedsvm.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 303 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 303 samples in 0.009s...\n",
      "[t-SNE] Computed conditional probabilities for sample 303 / 303\n",
      "[t-SNE] Mean sigma: 1.690220\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 63.303291\n",
      "[t-SNE] KL divergence after 300 iterations: 1.157215\n",
      "t-SNE done! Time elapsed: 0.7461647987365723 seconds\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import time\n",
    "\n",
    "time_start = time.time()\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(X)\n",
    "print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe22e2ef5f8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "sns.scatterplot(\n",
    "    x=tsne_results[:,0], y=tsne_results[:,1],\n",
    "    hue=Y,\n",
    "    palette=sns.color_palette(\"hls\", 2),\n",
    "    data=df,\n",
    "    legend=\"full\",\n",
    "    alpha=0.3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.21254053 0.11820708]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2,svd_solver='full')\n",
    "X_pca = pca.fit_transform(X)\n",
    "# print(pca.explained_variance_)\n",
    "\n",
    "# print('Original number of features:', X.shape[1])\n",
    "# print('Reduced number of features:', X_lda.shape[1])\n",
    "print(pca.explained_variance_ratio_)\n",
    "\n",
    "X_reduced, X_test_reduced, Y_Train, Y_Test = train_test_split(X_pca, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=2, random_state=0).fit(X_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[34 10]\n",
      " [ 5 42]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.77      0.82        44\n",
      "           1       0.81      0.89      0.85        47\n",
      "\n",
      "   micro avg       0.84      0.84      0.84        91\n",
      "   macro avg       0.84      0.83      0.83        91\n",
      "weighted avg       0.84      0.84      0.83        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kpredictions = kmeans.predict(X_test_reduced)\n",
    "print(confusion_matrix(Y_Test,kpredictions))\n",
    "print(classification_report(Y_Test,kpredictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe22c83f0f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test_reduced[kpredictions ==0,0], X_test_reduced[kpredictions == 0,1], s=100, c='red')\n",
    "plt.scatter(X_test_reduced[kpredictions ==1,0], X_test_reduced[kpredictions == 1,1], s=100, c='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# create dendrogram\n",
    "dendrogram = sch.dendrogram(sch.linkage(X_reduced, method='ward'))\n",
    "# create clusters\n",
    "hc = AgglomerativeClustering(n_clusters=2, affinity = 'euclidean', linkage = 'ward')\n",
    "# save clusters for chart\n",
    "hierarchicalpredictions = hc.fit_predict(X_test_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe22c584dd8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test_reduced[hierarchicalpredictions ==0,0], X_test_reduced[hierarchicalpredictions == 0,1], s=100, c='red')\n",
    "plt.scatter(X_test_reduced[hierarchicalpredictions ==1,0], X_test_reduced[hierarchicalpredictions == 1,1], s=100, c='black')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
